change order

README.md

@@ -17,6 +17,12 @@ This is a record of German language paraphrases. These are text pairs that have
 The source of the paraphrases are different parallel German / English text corpora.
 The English texts were machine translated back into German. This is how the paraphrases were obtained.
 
+## To-do
+- upload dataset
+- explain out preprocessing
+- suggest further postprocessing
+- explain dirty "texts" in OpenSubtitles
+
 ## Columns description
 - **`uuid`**: a uuid calculated with Python `uuid.uuid4()`
 - **`de`**: the original German texts from the corpus
@@ -28,12 +34,6 @@ The English texts were machine translated back into German. This is how the para
 - **`en_de_token_count`**: number of tokens of the `de` text, tokenized with [deepset/gbert-large](https://huggingface.co/deepset/gbert-large)
 - **`cos_sim`**: the [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) of both sentences measured with [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
 
-## Load this dataset with Pandas
-If you want to download the csv file and then load it with Pandas you can do it like this:
-```python
-df = pd.read_csv("train.csv")
-```
-
 ## Parallel text corpora used
 | Corpus name & link                                                    | Number of paraphrases |
 |-----------------------------------------------------------------------|----------------------:|
@@ -45,10 +45,6 @@ df = pd.read_csv("train.csv")
 | [GlobalVoices v2018q4](https://opus.nlpl.eu/GlobalVoices-v2018q4.php) |                70,547 |
 | **sum**                                                               |.       **21,292,789** |
 
-## To-do
-- add column description
-- upload dataset
-
 ## Back translation
 We have made the back translation from English to German with the help of [Fairseq](https://github.com/facebookresearch/fairseq).
 We used the `transformer.wmt19.en-de` model for this purpose:
@@ -90,6 +86,12 @@ def jaccard_similarity(text1, text2, somajo_tokenizer):
     return jaccard_similarity
 ```
 
+## Load this dataset with Pandas
+If you want to download the csv file and then load it with Pandas you can do it like this:
+```python
+df = pd.read_csv("train.csv")
+```
+
 ## Citations & Acknowledgements
 
 **OpenSubtitles**